KEYWORDS: Control systems, Control systems design, Coating, Fuzzy logic, Thin films, Optimization (mathematics), Solar cells, Picosecond phenomena, Solar energy, Resistance
With the consumption of global resources, photovoltaic technology has attracted worldwide attention as a clean energy source. Perovskite solar cells are recognized as one of the most promising new photovoltaic technologies because of their low cost and high efficiency. In the preparation of perovskite thin films by resistance thermal vacuum evaporator, the temperature change of the evaporation source is nonlinear, time-varying, and hysteresis. The traditional control methods have a slow response and large fluctuation, which cannot meet the precise and fast temperature control and affect the film quality. This paper proposes a fuzzy self-tuning PID evaporation source temperature control system based on Whale Optimization Algorithm (WOA). The WOA is combined with the segmentation control system together to form the WOA optimized control system. Compared with the common segmentation control algorithm, the improved algorithm reduces the response time by 16.1% and the regulation time by 23.2% while the overshoot is reduced by 75%, and the system can be stabilized with a better control effect even when the evaporation source model parameters are changed. The results show that the improved control system can improve the control effect of the system and reduce the response time and overshoot. The results show that the improved control system has effectively increased the efficiency of the vacuum coating.
Text classification is the most common application of Natural Language Processing(NLP), and Transformer models have dominated the field in recent years. Currently, pre-training modeling of text through deep learning methods is a common way of text classification. This paper firstly proposes an improved XLNet text classification model based on the problems of long-term dependence and insufficient contextual semantic expression in previous pre-trained language models, and uses XLNet pre-trained language modeling to represent text as low-dimensional word vectors to obtain sequences. Secondly, the generated word vector sequence is passed into the LSTM network, and the two-way features of the sentence are extracted by the memory unit of the LSTM. On the basis of effectively extracting text features, the Multi-head attention model is used to calculate the multi-angle attention probability, to focus on the important words. Finally, text classification is achieved through a fully connected network. The experimental results show that the accuracy of the model is 0.9457 and the loss rate is 0.3133. Compared with other related models, it has achieved better results.
Energy consumption model of urban rail transit is a complex nonlinear system, both the modeling study was not fully considering the problem of low precision and energy consumption model, according to the above problem, this paper proposed to improve the traditional dynamic model of the improved model compared with the traditional model is more accurate, more in line with the actual operation of train. On this basis, this paper explores the influence of distance between stations and running time on running energy consumption, and obtains the influence factor model through the MATLAB simulation platform. Finally, a simulation study was conducted on a section of Nanning Metro Line 1 as an example to verify the effectiveness and accuracy of the optimization model. The results show that the improved energy consumption model is 2.12% more accurate than the traditional model.
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